Character-Based BiLSTM-CRF Incorporating POS and Dictionaries for Chinese Opinion Target Extraction
Abstract
Opinion target extraction (OTE) is a fundamental step for sentiment analysis and opinion summarization. We analyze the difference between Chinese and the Indo-European languages family, and reduce Chinese OTE to a character-based sequence tagging task. Then we introduce two novel features for each character by distributing POS differentially and using predefined templates over contexts and dictionaries. We further propose a character-based BiLSTM-CRF model incorporating the two feature sequences aligned with the character sequence. Experimental results on real-world consumer review datasets show that our work significantly outperforms the baseline methods for Chinese OTE.
Cite
Text
Li et al. "Character-Based BiLSTM-CRF Incorporating POS and Dictionaries for Chinese Opinion Target Extraction." Proceedings of The 10th Asian Conference on Machine Learning, 2018.Markdown
[Li et al. "Character-Based BiLSTM-CRF Incorporating POS and Dictionaries for Chinese Opinion Target Extraction." Proceedings of The 10th Asian Conference on Machine Learning, 2018.](https://mlanthology.org/acml/2018/li2018acml-characterbased/)BibTeX
@inproceedings{li2018acml-characterbased,
title = {{Character-Based BiLSTM-CRF Incorporating POS and Dictionaries for Chinese Opinion Target Extraction}},
author = {Li, Yanzeng and Liu, Tingwen and Li, Diying and Li, Quangang and Shi, Jinqiao and Wang, Yanqiu},
booktitle = {Proceedings of The 10th Asian Conference on Machine Learning},
year = {2018},
pages = {518-533},
volume = {95},
url = {https://mlanthology.org/acml/2018/li2018acml-characterbased/}
}